Introduction
Mastering machine learning essentials
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What you should know
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1. Introduction to Machine Learning
Overview of types of machine learning
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Applications of ML
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Tools for ML
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Using GitHub Codespaces with this course
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2. EDA
Exploring the dataset
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Data preprocessing
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Scikit-learn pipelines
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Challenge: EDA plot
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Solution: EDA plot
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3. Model Creation
Dummy model
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Linear regression
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Decision trees
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CatBoost
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Challenge: Random forest pipeline
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Solution: Random forest pipeline
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4. Model Evaluation
R2
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Root mean squared
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Residual plot
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Challenge: Evaluate random forest
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Solution: Evaluate random forest
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5. Model Tuning
Hyperparameters and linear regression
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Tuning decision trees
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Tuning CatBoost
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Grid search
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Challenge: Tuning random forest
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Solution: Tuning random forest
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6. Model Deployment
End-to-end notebook
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Using MLFlow
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Challenge: MLFlow with random forest
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Solution: MLFlow with random forest
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